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Environmental Science and Pollution Research

, Volume 26, Issue 14, pp 13839–13853 | Cite as

Developing an electric vehicle urban driving cycle to study differences in energy consumption

  • Xuan ZhaoEmail author
  • Jian Ma
  • Shu WangEmail author
  • Yiming Ye
  • Yan Wu
  • Man Yu
Sustainable Environmental Management
  • 369 Downloads

Abstract

This paper develops a methodology for constructing a representative electric vehicle (EV) urban driving cycle as a basis for studying the differences in estimated energy consumption, taking Xi’an as an example. The test route is designed in accordance with the overall topological structure of the urban roads in the study region and the results of a traffic flow survey. Wavelet decomposition and reconstruction are utilized to preprocess the original data. Principal component analysis (PCA) is used to reduce the number of the kinetic parameters. The fuzzy C-means (FCM) clustering algorithm is used to cluster the driving segments. A representative EV urban driving cycle is constructed in accordance with the time proportions of three classes of driving segments and the correlation coefficients of the characteristic parameters. Finally, the differences in energy consumption estimates obtained using the constructed Xi’an EV urban driving cycle (XA-EV-UDC) and the international driving cycles are studied. The comparison shows that when international driving cycles are used to estimate the energy consumption and driving range of EVs, large relative errors will result, with energy consumption errors of 9.65 to 21.17% and driving range errors of 20.10 to 38.14%. Therefore, to accurately estimate energy consumption and driving range of EVs under real-world driving conditions, representative EV driving cycles for each typical city and region should be constructed.

Keywords

Driving cycle Electric vehicle Energy consumption Environmental pollution 

Notes

Funding

This research is funded by the National Key R&D Program of China (2017YFC0803904), National Natural Science Foundation of China (51507013), China Postdoctoral Science Foundation (2018T111006, 2017M613034), Postdoctoral Science Foundation of Shaanxi Province (2017BSHEDZZ36), Shaanxi Province Industrial Innovation Chain Project (2018ZDCXL-GY-05-03-01), Shaanxi Provincial Key Research and Development Plan Project (2018ZDXM-GY-082), and Shaanxi Innovative Talents Promotion Plan Project (2018KJXX-005).

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Copyright information

© Springer-Verlag GmbH Germany, part of Springer Nature 2018

Authors and Affiliations

  1. 1.School of AutomobileChang’an UniversityXi’anChina

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